Snooze - Reddit AI Agent Discussion Analyzer

Snooze is a Python application that analyzes and visualizes Reddit discussions about AI coding tools.

It focuses on specific coding-related subreddits (r/ClaudeCode, r/codex, r/GithubCopilot, r/ChatGPTCoding), uses LLM to summarize discussions, and includes intelligent caching to avoid repeated API calls.

How Posts Are Selected

Snooze uses a multi-stage filtering process to identify relevant AI coding discussions:

1. Target Subreddits: Focuses on AI coding tool communities (r/vibecoding, r/ClaudeCode, r/codex, r/GithubCopilot, r/ChatGPTCoding, r/cursor)

2. Keyword Filtering: Posts must contain coding-related keywords like "copilot", "claude", "chatgpt", "cursor", "coding", "ai assistant", "pair programming", etc.

3. Engagement Priority: Fetches "hot" posts (most popular recent posts) and sorts by score and recency

4. Enhanced LLM Relevance Filtering: Each post is analyzed by LLM with strict criteria to exclude:

   • ❌ Subreddit rules, guidelines, or meta announcements
   • ❌ Moderator posts about community features or policies
   • ❌ Empty posts, memes, or low-effort content
   • ❌ General AI discussions without coding/development focus
   • ✅ Only includes substantive discussions about AI coding tools, workflows, and experiences

Live Progressive Rendering

The web interface provides real-time feedback during analysis:

• Instant display: Cached summaries appear immediately (0.1 seconds)
• Live streaming: New analyses stream in real-time as completed
• Progressive results: Users see content as soon as it's available
• No waiting: No need to wait for full batch completion

Example: When analyzing 20 posts where 18 are unchanged:

• ❌ Old system: 20 LLM calls, ~2-3 minutes, batch results
• ✅ New system: 2 LLM calls, ~15 seconds, live streaming

User Experience: Instead of staring at a loading screen for minutes, users see 90% of results instantly, then watch new ones appear every 15-30 seconds.

Cache files are stored in data/ directory with MD5-hashed keys based on input parameters. Individual post summaries are stored as data/post_summaries/[post_id].json.

Installation

This project uses uv for dependency management:

# Clone the repository
git clone https://github.com/Arist12/snooze.git
cd snooze

# Install dependencies
uv sync

Configuration

Create a .env file in the project root with your API credentials:

# Reddit API (required for crawling)
REDDIT_CLIENT_ID=your_reddit_client_id
REDDIT_CLIENT_SECRET=your_reddit_client_secret

# Azure OpenAI (required for analysis)
AZURE_API_KEY=your_azure_openai_key
AZURE_ENDPOINT=https://your-resource.openai.azure.com
AZURE_DEPLOYMENT=your_deployment_name

Getting Reddit API Keys

Reddit API:

1. Go to https://www.reddit.com/prefs/apps
2. Create a new app (script type)
3. Use the client ID (at the bottom of your app's name) and secret

Usage

Web Interface (Recommended)

Start the web server:

snooze web

Open http://localhost:8080 in your browser and use the interface to analyze discussions.

The web interface provides control over analysis scope:

• Posts to Crawl (10-200): Total posts to fetch from Reddit

Command Line

Analyze discussions:

snooze analyze --subreddits artificial MachineLearning ChatGPT --limit 20

Crawl posts without analysis:

snooze crawl --subreddits artificial --limit 50 --search "AI agent"

Get help:

snooze --help

Project Structure

snooze/
├── src/snooze/
│   ├── __init__.py           # Package initialization
│   ├── crawler.py            # Reddit crawling functionality
│   ├── summarizer.py         # LLM analysis and summarization
│   ├── visualizer.py         # Flask web application
│   ├── main.py              # CLI entry point
│   ├── templates/           # HTML templates
│   │   └── index.html
│   └── static/              # CSS and JavaScript
│       ├── css/style.css
│       └── js/app.js
├── example.py               # Example usage of APIs
├── test_pipeline.py         # Pipeline testing script
├── pyproject.toml          # Project configuration
└── README.md

Attribution

We thank Reddit for providing the data and Claude Code for initiating the codebase.